Evidence Accumulation Modeling: Bayesian Estimation using Differential Evolution

Abstract

Understanding decision making requires a dynamic approach that accounts for the time taken to make choices as well as the choices that are made. The success of the dynamic approach is underpinned by cognitive models, such as the drift-diffusion model (DDM: Ratcliff & McKoon, 2008) and linear ballistic accumulator (LBA: Brown & Heathcote, 2005, 2008), that attribute decisions to an evidence accumulation processes. The ability of these models, and elaborations of them, to account for the speed and accuracy with which people make decisions across a broad range of tasks has led to an increasing number of applications in Cognitive Science and Neuroscience (for recent examples see Cassey, Heathcote & Brown, 2014; Heathcote, Loft & Remington, in press; Mittner et al., 2014; Turner, Van Maanen, Forstmann, in press).


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